Self-report tools for measuring energy intake (EI) have a number of limitations, including high user and experimenter burden, interference with usual eating habits, decreased compliance over time, and underreporting bias. While the technology of wearable sensors has significantly advanced the assessment of energy expenditure (EE) in the form of accelerometer-based physical activity monitors, the development of a similar tool for monitoring EI has remained elusive. Previous research has been limited to testing in the laboratory using cumbersome measurements of sound and muscle activity at the throat and ear, and multiple sensors tracking torso and limb movements. Our group is developing technologies for monitoring eating using sensors worn on the wrist, like a watch. We have developed novel methods to detect when the user is eating by continuously tracking wrist motion throughout the day and recognizing patterns of wrist motion that are distinctive of eating. We refer to a period of contiguous consumption as an eating activity (EA). During each detected EA, we have developed a method to measure the number of bites taken, where a bite is the action of placing food into the mouth, which can be used as a proxy for portion size. By combining bites with a kilocalorie per bite (KPB) modifier, we can estimate EI as bites multiplied by KPB. The significance of our work is that the wrist worn technologies we are developing have the potential to increase EI monitoring accuracy and compliance in free-living conditions at a reduced per person cost. If successful, the wrist worn measures could impact EA research by affording increased EA data collection from a greater diversity of people in a wider variety of situations. The measures could also impact weight loss interventions by providing a novel self-monitoring and feedback tool to the individual. Finally, the measures could impact clinical assessment of eating patterns and EI in situ, similar to the way Holter monitors impacted the clinical evaluation of cardiac arrhythmias. The innovation of our approach is that we are tracking wrist motions to identify and measure periods of eating. Previous research has focused on sensors worn on the throat and ear to detect vibrations and sounds related to eating. However, wrist-worn sensors are more socially acceptable than head-mounted sensors due to their discreet appearance and resemblance to the common bracelet or watch. The challenge to be met in the proposed work is to use EA context to improve and further validate measures of energy and nutrient intake. Our preliminary work used only wrist motion to detect EAs and a person's demographics to calculate KPB. The current work will use additional context to improve EA detection and the accuracy of the KPB modifier in estimating EI. Specifically context derived from clock measures (time of day, time since last EA), demographics (gender, age, height, weight) and foods selected will be studied to determine the effect of their inclusion on improving both EA detection and EI measurement. This is similar in concept to how an exercise monitor can improve its measure of EE when calibrated with information about the context of the person and nature of the EE activity.